Course overview

Regression for Linguists

Autor:in

Zugehörigkeit

Daniela Palleschi

Humboldt-Universität zu Berlin

Veröffentlichungsdatum

25. September 2023

Error: not found: Winter_2013, Winter_2014, sondregger_regression_2023, baayen_2008, jaeger_2008, barr_2013, matschucek_2017

Moodle

Moodle: “Regression for Linguists”

Schlüssel: reg4ling

Moodle

lecture materials

Kursübersicht

Leistungspunkte
- 3LP
  - 1LP:
  - 1LP:
  - 1LP:
Office hours: Wednesdays, 15.00-16.00 (by appointment)

Course aims

By the end of this course, you will

blah blah

What will you learn?

linear regression
multiple regression
logistic regression
mixed models
using the lme4 package
how to apply these models appropriately to a variety of data types

What will you not learn?

Resources

This course is mainly based on (winter_statistics_2019?), which is an excellent introduction into regression for linguists. For even more introductory tutorials, I recommend going through (Winter_2013?) and (Winter_2014?). For a more intermediate textbook, I’d recommend (sondregger_regression_2023?).

If you’re interested in the foundational writings on the topic of linear mixed models in (psycho)linguistic research, I’d recommend reading (baayen_2008?); (jaeger_2008?); (barr_2013?); (matschucek_2017?).

Assumptions about you

For this course, I assume that you are familiar with more classical statistical tests, such as the t-test, Chi-square test, etc. I also assume you are familiar with measures of central tendency (mean, median, mode) measures dispersion/spread (standard deviation), and with the concept of a normal distribution. Lacking this knowledge will not impeded your progress in the course, but is an important foundation on which we’ll be building. We can review these concepts in-class as needed.

Software

R: a statistical programming language (the underlying language)
RStudio: an program that facilitates working with R; our preferred IDE integrated development environment
LaTeX: a typesetting system that generates documents in PDF format
why R?
- R and RStudio are open-source and free software
- they are widely used in science and business

Install R

we need the free and open source statistical software R to analyze our data
download and install R: https://www.r-project.org

Install RStudio

we need RStudio to work with R more easily
Download and install RStudio: https://rstudio.com
it can be helpful to keep English as language in RStudio
- we will find more helpful information if we search error messages in English on the internet
If you have problems installing R or RStudio, check out this help page (in German): http://methods-berlin.com/wp-content/uploads/Installation.html

Install LaTeX

we will not work with LaTeX directly, but it is needed in the background
Download and install LaTeX: https://www.latex-project.org/get/

resources

many aspects of this course are inspired by (nordmann_applied_2022?) and (wickham_r_nodate?)
- both freely available online (in English)
for German-language resources, visit the website of Methodengruppe Berlin

Troubleshooting (EN: Troubleshooting)

Error messages are very common in programming, at all levels.
How to find solutions for these error messages is an art in itself
Google is your friend! If possible, google in English to get more information

Literaturverzeichnis

--- subtitle: "Regression for Linguists" author: "Daniela Palleschi" institute: Humboldt-Universität zu Berlin # footer: "Lecture 1.1 - R und RStudio" lang: de date: "`r Sys.Date()`" format: html: output-file: kursuebersicht_blatt.html number-sections: true number-depth: 3 toc: true code-overflow: wrap code-tools: true self-contained: true fig-width: 6 pdf: output-file: course_overview.pdf toc: true number-sections: true colorlinks: true fig-width: 4 code-overflow: wrap bibliography: references.bib csl: apa.csl execute: eval: true # evaluate chunks echo: true # 'print code chunk?' message: false # 'print messages (e.g., warnings)?' error: true # ignore errors when rendering? warning: false --- # Course overview {.unnumbered} ```{r, eval = T, cache = F} #| echo: false # Create references.json file based on the citations in this script # make sure you have 'bibliography: references.json' in the YAML rbbt::bbt_update_bib("00-course_overview.qmd") ``` # Moodle Moodle: "Regression for Linguists" Schlüssel: reg4ling ## Moodle {-} - lecture materials # Kursübersicht - Leistungspunkte - 3LP - 1LP: - 1LP: - 1LP: - Office hours: Wednesdays, 15.00-16.00 (by appointment) ## Course aims By the end of this course, you will - blah blah ### What will you learn? - linear regression + multiple regression + logistic regression + mixed models - using the `lme4` package - how to apply these models appropriately to a variety of data types ### What will you *not* learn? - # Resources This course is mainly based on @winter_statistics_2019, which is an excellent introduction into regression for linguists. For even more introductory tutorials, I recommend going through @Winter_2013 and @Winter_2014. For a more intermediate textbook, I'd recommend @sondregger_regression_2023. If you're interested in the foundational writings on the topic of linear mixed models in (psycho)linguistic research, I'd recommend reading @baayen_2008; @jaeger_2008; @barr_2013; @matschucek_2017. # Assumptions about you For this course, I assume that you are familiar with more classical statistical tests, such as the t-test, Chi-square test, etc. I also assume you are familiar with measures of central tendency (mean, median, mode) measures dispersion/spread (standard deviation), and with the concept of a normal distribution. Lacking this knowledge will not impeded your progress in the course, but is an important foundation on which we'll be building. We can review these concepts in-class as needed. # Software {#sec-software} - R: a statistical programming language (the underlying language) - RStudio: an program that facilitates working with R; our preferred IDE integrated development environment - LaTeX: a typesetting system that generates documents in PDF format - why R? - R and RStudio are open-source and free software - they are widely used in science and business ::: {.content-hidden when-format="pdf"} ::: {.column width="30%"} ```{r eval = F, fig.env = "figure", out.width="50%", fig.align = "center"} #| echo: false magick::image_read(here::here("media/R_logo.png")) ``` ::: ::: {.column width="30%"} ```{r eval =F , fig.env = "figure", out.width="75%", fig.align = "center"} #| echo: false magick::image_read(here::here("./media/RStudio_logo.png")) ``` ::: ::: ```{r eval = F, fig.env = "figure", out.width="75%", fig.align = "center"} #| echo: false magick::image_read(here::here("./media/LaTeX_logo.png")) ``` ::: {.content-visible when-format="pdf"} ```{r eval = F, fig.env = "figure", fig.pos="H", out.width="75%", fig.align = "center"} #| echo: false R <- grid::rasterGrob(as.raster(png::readPNG(here::here("./media", "R_logo.png")))) RStudio <- grid::rasterGrob(as.raster(png::readPNG(here::here("./media", "RStudio_logo.png")))) latex <- grid::rasterGrob(as.raster(png::readPNG(here::here("./media", "LaTeX_logo2.png")))) gridExtra::grid.arrange(R, NULL, RStudio, NULL, latex, ncol=5, widths=c(.25,.125,.25,.125,.25)) ``` ::: ## Install R - we need the free and open source statistical software R to analyze our data - download and install R: <https://www.r-project.org> ## Install RStudio - we need RStudio to work with R more easily - Download and install RStudio: <https://rstudio.com> - it can be helpful to keep English as language in RStudio - we will find more helpful information if we search error messages in English on the internet - If you have problems installing R or RStudio, check out this help page (in German): <http://methods-berlin.com/wp-content/uploads/Installation.html> ## Install LaTeX - we will not work with LaTeX directly, but it is needed in the background - Download and install LaTeX: <https://www.latex-project.org/get/> # resources - many aspects of this course are inspired by @nordmann_applied_2022 and @wickham_r_nodate - both freely available online (in English) - for German-language resources, visit the website of [Methodengruppe Berlin](http://methods-berlin.com/de/r-lernplattform/) ## Troubleshooting (EN: Troubleshooting) - Error messages are very common in programming, at all levels. - How to find solutions for these error messages is an art in itself - Google is your friend! If possible, google in English to get more information # Literaturverzeichnis {.unlisted .unnumbered visibility="uncounted"} ::: {#refs custom-style="Bibliography"} :::